Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI.

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Lean body weight versus total body weight to calculate the iodinated contrast media volume in abdominal CT: a randomised controlled trial.

OBJECTIVES: Iodinated contrast media (ICM) could be more appropriately dosed on patient lean body weight (LBW) than on total body weight (TBW). METHODS: After Ethics Committee approval, trial registration NCT03384979, patients aged ≥ 18 years scheduled for multiphasic abdominal CT were randomised for ICM dose to LBW group (0.63 gI/kg of LBW) or TBW group (0.44 gI/kg of TBW). Abdominal 64-row CT was performed using 120 kVp, 100-200 mAs, rotation time 0.5 s, pitch 1, Iopamidol (370 mgI/mL), and flow rate 3 mL/s. Levene, Mann-Whitney U, and χ2 tests were used. The primary endpoint was liver contrast enhancement (LCE). RESULTS: Of 335 enrolled patients, 17 were screening failures; 44 dropped out after randomisation; 274 patients were analysed (133 LBW group, 141 TBW group). The median age of LBW group (66 years) was slightly lower than that of TBW group (70 years). Although the median ICM-injected volume was comparable between groups, its variability was larger in the former (interquartile range 27 mL versus 21 mL, p = 0.01). The same was for unenhanced liver density (IQR 10 versus 7 HU) (p = 0.02). Median LCE was 40 (35-46) HU in the LBW group and 40 (35-44) HU in the TBW group, without significant difference for median (p = 0.41) and variability (p = 0.23). Suboptimal LCE (< 40 HU) was found in 64/133 (48%) patients in the LBW group and 69/141 (49%) in the TBW group, but no examination needed repeating. CONCLUSIONS: The calculation of the ICM volume to be administered for abdominal CT based on the LBW does not imply a more consistent LCE.

Burden of Exposure to Potential Interactions Between Antiretroviral and Non-Antiretroviral Medications in a Population of HIV-Positive Patients Aged 50 Years or Older.

BACKGROUND: As HIV-infected patients aged 50 years or older are at increased risk of comorbidities and multidrug treatments, we examined their exposure to the potential drug-drug interactions (PDDIs) of antiretroviral (ARV) and other medications. METHODS: This cross-sectional study involved the patients aged 50 years or older receiving ARV and non-ARV medications at our clinic. PDDIs were identified using the University of Liverpool HIV Drug Interaction Checker. Logistic regression models were used to assess risk factors for PDDIs. The American Geriatrics Society Beers Criteria were used to identify potentially inappropriate medications (PIMs). RESULTS: A total of 395 (53.9%) of 744 patients showed ≥1 PDDI: 47.4% ≥ 1 amber-PDDI (comedications requiring appropriate management) and 5.6% ≥ 1 red-PDDI (contraindicated comedications). A higher risk of PDDIs was associated with the use of ≥5 medications (P < 0.001), of antiosteoporotics (P < 0.001), calcium channel blockers (P < 0.001), anti-benign prostatic hypertrophy agents (P < 0.001), hypnotics/sedatives (P = 0.022), and anticoagulants (P = 0.006). A higher risk of red-PDDIs was associated with the use of antacids (P < 0.001), anti-benign prostatic hypertrophy agents (P < 0.001) and antipsychotics (P = 0.023). The use of nucleoside reverse transcriptase inhibitor + nonnucleoside reverse transcriptase inhibitor and nucleoside reverse transcriptase inhibitor + integrase strand transfer inhibitor rather than protease inhibitor-based regimens was associated with a reduced risk of PDDIs (P < 0.001). Overall, 119 (16.0%) patients were receiving PIMs (mainly hypnotics/sedatives) and 49 (41.2%) of them had PDDIs able to increase the blood levels of these medications. CONCLUSIONS: Older patients with HIV are highly exposed to PDDIs between ARVs and comedications. The knowledge of their complete medication regimens and the screening for PDDIs and PIMs is therefore crucial to prevent drug-related adverse outcomes in this population.